ABSTRACT Approximately 15% of toddlers 18-36 months of age experience late language emergence (LLE; Paul, 1992; Singleton, 2018). These late talkers (LTs) have a reduced expressive vocabulary, but average non-linguistic abilities, in the absence of overt sensory or other developmental delays (Collisson, 2016; Paul & Jennings, 1992). Upwards of 16% of LTs prospectively meet criteria for language disorder (Rescorla, 2009), while others retain suboptimal language functioning (Rescorla, 2002; Singleton, 2018). LTs are at elevated risk for lifelong language and literacy impairments that negatively impact access to academic and vocational opportunities (Singleton, 2018; Paul, 1993), and even subclinical outcomes have pervasive negative impacts (Rescorla, 2002). This project addresses questions crucial for the early diagnosis of LTs and prerequisite to the applicant?s long-term goal of establishing an independent research program on LLE, aimed ultimately at identifying variation and distribution of behavioral phenotypes to provide a foundation for more targeted interventions for LTs. This project complements prior work on LLE focused on language production by evaluating the time course of word learning and spoken word recognition in LTs and 2 control groups (age- and language-matched typically developing peers), all of whom will complete standardized assessments of cognitive -linguistic abilities. In Expt. 1, participants will train on a simple selection task using 4 novel and 4 familiar words that overlap phonologically (e.g., at onset, BUNNY-BUTTON, or offset, KITTEN-MITTEN). We will use eye tracking to estimate group and individual differences in lexical activation and competition over time. In Expt. 2, we will record EEG (electroencephalography) in a passive listening task. Participants will watch a silent video as newly-learned and familiar words from Expt. 1 are repeated. ERP (event-related potential) analyses will examine individual and group differences in responses to newly-learned vs. familiar words. We will also use machine-learning (support vector machines, SVMs) to decode EEG responses to specific words for each participant, on the logic that fidelity and coherence of responses will determine SVM classification success. Group and individual differences in eye tracking, ERP, and/or EEG-decoding measures will provide new insights into receptive abilities of LTs, and provide a basis for future work aimed at identifying LTs with greatest risk for clinical or subclinical language outcomes. The project will take place at the U. of Connecticut and Haskins Labs. The applicant and sponsors have developed a training plan for the applicant focused on further developing her (1) EEG and statistical skills, (2) knowledge base of the cognitive neuroscience of typical and atypical language development, (3) dissemination skills, and (4) understanding of principles for the responsible conduct of research, with the aim of supporting her goal to be an independent researcher in a Research-1 environment.